Research Journal of Recent Sciences ______ ______________________________ ______ ____ ___ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent . Sci. International Science Congress Association 1 Optimal Cropping Pattern for Jaisamand command of Udaipur district in Rajasthan, India Pravin Dahiphale 1* , P.K.Singh 1 , Mahesh Kothari 1 and Kishor Gharde 2 1* SWE Deppt ., CTAE, MPUAT, Udaipur, INDIA 2 SWE Deppt ., CAET, Dapoli, INDIA Available online at: www.isca.in , www.isca.me Received 20 th November 201 4 , revised 22 nd December 201 4 , accepted 10 th January 201 5 Abstract This paper presents an application of LINGO software to allocate the area for production maximization in Jaisamand command area of Udaipur district. The linear programming model was developed and solved in LINGO software. The area allocated for different crop activities in 9,18,21,24 and 30 canal running days was obtained. The optimal food production for maize, soyabean, moong, wheat, mustard, gram and barley in 9,18,21,24 and 30 canal running days obtained as 33454.94, 70278.44, 68502.53, 71987.65 and 72082.02 tonnes with investment of 403.00, 773.78, 797.67, 845.09 and 851 .22 million Rs. respectively. The net benefit obtained as 219.55, 58.02, 451.89, 456.06 and 455.43 million Rs. for 9,18,21,24 and 30 canal running days respectively. Keywords: Optimal, Jaisamand command, LINGO . Introduction To fulfil the high demand fo od fibre and fuel to an increasing population it is necessary to bring more area under cultivation or to increase production per unit area of available land and water resources. Due to urbanization and a reluctance to disturb natural environments there is difficult to bring the additional area under cultivation. Therefore, it is important to optimize the available land and water resources to achieving maximum production. The existing cropping pattern has been same for many years and may not utilize resource s at maximum economic efficiency. Linear programming model can handle a large number of constraints and thus, are an effective tool to aid in the optimization process. Some of the reviews about optimal allocation of canal water are discussed below. Santh i and Pundarikanthan suggested a new planning model for canal scheduling of rotational irrigation 1 . Srinivas and Nagesh developed a linear programming (LP) irrigation planning model for the evaluation of irrigation development strategy and applied to a cas e study of Sri Ram Sagar project, Andhra Pradesh, India with the objective of maximization of net benefit 2 . Anwar and Clarke presented a mixed integer program for scheduling canal irrigation among a group of users where the users specified the duration of flow of each outlet and a target start time 3 . Vries and Arif presented an integer program solution for sequential irrigation scheduling problem of two different models to reflect different management options at the tertiary level 4 . Bhabagrahi S. and Anil K.L. developed a linear programming and fuzzy optimization models for planning and management of available land - water crop system of Mahanadi - Kathajodi delta in Eastern India 5 . Khare et al . developed conjunctive use linear programming model for planning in a link canal command area 6 . Brian and Marshall studied the use of a coupled groundwater simulation and optimization model to guide groundwater management in the upper Klamath basin, Oregon and California 7 . Saafan et al . carried out study on a multi - object ive optimization approach to groundwater management using genetic algorithm 8 . Ajay Singh carried out study on optimization modelling applications. The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided in his paper 9 . Regulwar and Pradhan developed fuzzy Linear programming model by using surface and groundwater for irrigation planning 10 . Li an d Guo used a multi - objective optimal allocation model for irrigation water resources under multiple uncertainties 11 . Raul et al. developed conjunctive use planning model for opt imal cropping under hydrological uncertainty 12 . Material and Methods The Jaisamand Lake was constructed by the rular of Mewar in the year 1711 - 1730, near village Veerpura, Tehsil Sarada, Disrict Udaipur. The lake was constructed for wildlife and recreatio n but after independence, the canal system is developed and about 16000 ha area included as command area. The detail information is given in table - 1. The command area is having good soil characteristics and two crops (Kharif, Rabi) can be grown up. Exist ing cropping patterns: In Jaisamand command area general crops like maize, soyabean, moong, wheat, mustard etc. are grown. The total cultivable area is 17900 in kharif and rabi season. The total production obtained is 43446.2 tonnes with investment of 469. 74 million Rs. and net benefit obtained is 275.72 million Rs. as shown in table - 2. The cost of cultivation Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 2 of different crops collected from Directorate of Economics and Statistics, Udaipur (Rajasthan). The command area population data collected from Sarad a and Salumber Tehsil of Udaipur district. The month wise data of labour requirement of different crops collected from Water resource Department Salumber. Monthly crop water requirement was calculated on the basis of FAO - 56 by using thirty four years pan e vaporation data. The details of monthly crop water requirement of different crops as shown in table - 3. The monthly water available in LMC and RMC of canal running different days as shown in table - 4. Linear programming model: The linear programming mode l consisting of three major components: an objective function for maximization of production, a set of linear constraints and a set of non - negativity constraints was developed. The model was formulated to allocate land among the different crops, in order t o maximize the production from the command area. The crop model developed is solved using LINGO package. The water supply available at inlet was considered as the only source of available water in the command. Objective Function: Production Maximization : The production is to be maximized in order to safeguard the interest of the country so that self - sufficiency in food production is achieved. Where : P j Stands for production of j th crop activity in q/ha X ij stands for the area under i th can al, j th crop activity in ha i = 1 to 2 (LMC and RMC) Constraints: A planning should take care of needs of the people. To take care of all these factors, the following constraints need to be imposed. Area constraints: The area under cultivation in Kharif and Rabi season cannot exceed the total cultivable area. This can be mathematically expressed as under, Where : A= stands for total cultivable area in the project command area in ha Water constraints: Water requirement for different crop m ust be less than or equal to the water resources available during the season. If W jt , X ij represents the product of water requirement per hectare and the area under j th crop activity in the t th month, then for t= 1,2,3…….12 Labour re quirement constraints: Labour requirement for different crops on the field in a particular month must be less than or equal to the labour - days available in the month so that there will not be any need to bring labour from outside. If l jt represents the la bour requirement for j th crop in t th month in a growing season, for t = 1,2,3…….12 Food requirement constraint : Total production of Maize, Soyabean, Moong, Wheat, Mustard, Gram and Barley should meet the actual requirement of the total population of th e command area. These are the social constraints and can be expressed as Where : P j stands for yield of j th crop activity in q/ha , P f stands for bulk requirement of food in quintal . Results and D iscussion Considering the objective of food p roduction maximization, the area allocated to different crop activities found out for various levels of water availability i.e. number of canal running days (9,18,21,24,30 days). The area allocated to different crop activities in kharif and rabi season ar e presented in table - 5. The optimal food production for maize, soyabean, moong, wheat, mustard, gram and barley in 9,18,21,24 and 30 canal running days obtained as 33454.94, 70278.44, 68502.53, 71987.65 and 72082.02 ton with investment of 403.00, 773.78, 7 97.67, 845.09 and 851.22 million Rs. respectively. The net benefit obtained as 219.55, 58.02, 451.89, 456.06 and 455.43 million Rs. for 9,18,21,24 and 30 canal running days respectively. Table - 5 shows that the area allocated for different crop activities w ith available water by using linear programming model. Figure - 2 shows that in the command area maximum area under wheat then maize and small amount of barley crop taken for cultivation. But after developing linear programming model for production maximizat ion there is observed changes in cropping pattern. From figure - 3 it is observed that the area allocated for the soyabean is more than gram, wheat, barley, moong, mustard and maize for 9 days canal running for production maximization. In canal running 18 d ays area allocated for soyabean and wheat is more than other crops. Mustard crop allocated least area ( figure - 3). Linear programming model is developed for the 21 canal running days and area allocated is more for wheat then maize, soyabean, mustard, barley , moong and gram ( figure - 3). In 24 canal running days, maize crop allocated more area then wheat, soyabean, moong, gram, barley and small amount of mustard crop ( figure - 3). In similar way solution of linear programming model in LINGO - package shows the area allocated for maize and wheat crop is more than other crops for canal running in 30 days. Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 3 Figure - 1 Location map of Command area Figure - 2 Existing cropping pattern in the command area Figure - 3 Optimal allocation of surface water in LMC and RMC for different canal running days Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 4 Table - 1 Description of canal network Sr.no. Particular Remarks 1 Location of site I .State Rajasthan ii. District Udaipur iii. Tehsil Sarada iv. Village Gatod v. Longitude 73 o 57’10”E Vi. Latitude 24 o 14’30”N 2 Hydrology i. Name of river Gomati ii. Gross catchment area 1858 sq.km. iii. Catchment area intercepted 159 sq.km. iv. Free catchment area 1654 sq.km v.75% dependable monsoon rainfall 556mm. vi.75% dependable runoff yield from free C.A. 6 4.24M.cum. vii.50% dependable yield 155.45M.cum. viii. Maximum probable flood 18876 cumecs Routed flood 5405 cumecs 3 Utilization 1. Irrigation i. G.C.A. 37282 ha ii. C.C.A. 16000ha iii. .Annual irrigation 14400ha iv. Additional 8353ha. 2. Irrigation utilisation i. Kharif 25.86 M.cum. ii. Rabi 58.44 M.cum. Total 84.30 M.cum. Total evaporation 54.70 M.cum. Total utilisation 139.00 M.cum. Duty - 7.25Ac/M.cum. 4. Storage planning i. Gross storage 414.60 M.cum. ii. Live storage 296.14 M.cum. iii. Dead storage 118.46 M.cum. 5 Control elevations i. T.B.L. 303.10 m ii. M.W.L. 301.10 m iii. F.R.L. 295.47 m iv. Crest level of spillway 295.47 m v. M.D.D.L. 287.70 m 6 Submergence detail i. Area under submergenc e at F.R.L. 5260 ha ii. Culturable area under submergence 2752 ha iii. Submergence ratio with respect to C.C.A 32.88% 7 Dam i. Type of dam Composite section consisting of two massive masonry walls on U/s and D/s faces earth filling in between ii. Length of dam 399 m iii. Top width of dam 94 m to 100 m iv. Maximum height above bed level of river 42.06 m v. Free board above M.W.L. 2 m Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 5 Sr.no. Particular Remarks 8 Spillway i. Gated spillway Sill R.L. of gates 2 no. gates (3.055.03) 289.97 ii. Type of spillway Ungated situated on L/S of main dam in three saddles iv. Byewash in saddles Three saddles Saddle no.1 30.50 m Saddle no.2 90 m Saddle no.3 20 m Total 140.50 m v. Crest level 295.47 m vi. Discharging capacity 5405 cumecs 9 Canals Type of canals Lined Length of main canal Left canal 51.09 km Right canal 22.86 km 10 Discharge at head Left canal 7.56 cumecs Right canal 1.53 cumecs 11 Free board Left canal 0.60 m Right canal 0.30 m 12 Side slope Le ft canal Vertical Right canal Vertical 13 Bed levels Left canal 287.16 m Right canal 289.83 m 14 Bed width Left canal 3 m Right canal 2.45 m 15 Full supply depth Left canal 1 m Right canal 0.72 m Table - 2 Existing cropping pattern in command area Sr. No. Crop Activity Area under the crop (ha) Kharif 1 Maize 4800 2 Soyabean 500 3 Moong 1300 Rabi 4 Wheat 8000 5 Mustard 1600 6 Gram 1400 7 Barley 300 Total (ha) 17900 Investment in million Rs. 469.74 Achievement level Production in tonnes 43446.2 Labour in man - day 1601000 Net Benefit in million Rs. 275.72 Source: Water resource Deparment Salumber Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 6 Table - 3 Monthly water requirement of different crops (cm) Month Maize Soyabean M oong Wheat Mustard Gram Barley Jan 6.3 5.98 5.61 7.51 Feb 8.84 4.18 2.51 12.62 March 8.45 17.00 April 4.37 May June July 3.07 6.59 1.75 August 6.74 8.07 4.00 Sept 12.86 8.03 8.16 Oct 3.77 4.73 7.47 1.67 2.56 Nov 1.4 3.67 4.98 Dec 5.38 7.46 7.42 0.38 Total 26.44 27.42 21.38 30.37 22.96 23.08 41.88 Table - 4 Canal water available in different days in the month (ha - cm) Table - 5 Optimal allocation of surface water using linear programming model for production maximization in different canal running days Crop Number of canal running days 9 18 21 24 30 LMC (Area allocated) K harif Maize 781.20 2692.63 6755.04 10817.45 10910.82 Soyabean 4729.98 8999.39 4936.98 874.57 781.20 Moong 1327.97 1327.97 1327.97 1327.97 1327.97 Rabi Wheat 2743.13 9462.41 8115.02 9462.41 9462.41 Mustard 781.20 781.20 2128.58 781.20 781.20 Gram 4 434.43 1174.31 1174.31 1174.31 1174.31 Barley 1602.07 1602.07 1602.07 1602.07 1602.07 RMC Kharif Maize 178.80 178.80 783.29 1604.36 2408.65 Soyabean 823.24 2304.84 1830.61 1009.54 205.25 Moong 366.08 366.04 366.08 366.08 366.08 Rabi Wheat 519.43 1824.81 2035.82 2035.82 2035.85 Mustard 178.80 178.80 178.80 178.80 178.80 Gram 392.21 534.43 323.72 323.72 323.72 Barley 441.65 441.65 441.65 441.65 441.65 Total (ha) 19300.19 31869.35 31999.94 31999.95 31999.98 Investment in million Rs. 4 03.00 773.78 797.67 845.09 851.22 Achievement level Production in tonnes 33454.94 70278.44 68502.53 71987.65 72082.02 Labour in man - day 1455642 2867726 2769329 2788370 2778499 Net Benefit in million Rs. 219.55 58.02 451.89 456.06 455.43 Days 9 18 21 24 30 LMC 58864.32 117728.64 137350.08 156971. 196214.4 RMC 11 897.28 23794.56 27760.32 31726.08 39697.6 Research Journal of Recent Sciences ______ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 4 ( ISC - 201 4 ), 1 - 7 (201 5 ) Res. J. Recent. Sci. International Science Congress Asso ciation 7 Conclusion In the present study linear programming model is developed for the production maximization and solved in LINGO software tool. The area allocated for wheat crop for canal running days 24 and 30 days is maximum. Mustard crop allocated same area in 9, 18, 24 and 30 canal running days in the month. Wheat, soyabean and maize are the major crops for which maximum area allocated for the production. Net benefit obtained as 455.43 million Rs. for 30 canal running days. So for achieving maximum production wheat, soya bean and maize crops taken for cultivation in allocated area. Acknowledgement The first author is thankful to DST for providing financial support through INSPIRE Fellowship during research work. References 1. Santhi and Pundarikanthan, New planning model fo r canal sche duling of rotational irrigation, Agricultural Water management, 43(3), 327 - 343 (2000) 2. Srinivas and Nagesh, Optimum cropping pattern for Sri Ram Sagar project: a linear programming approach , J. Appl. Hydro., XII (1and2), 57 - 67 (2000). 3. Anwar A. A. and Clarke, D., Irrigation scheduling using mixed integer linear programming , J. Irri.and Dra. Engg. , ASCE , 127 (2) , 63 - 69 (2001) 4. Vries T. and Anwar A., Irrigation shedulling, Integer Programming Approach , J . Irri. and Dra. Engg. 1 30(1), 9 - 16 (2004) 5. B habagrahi S. and Anil K., Fuzzy Multi objective and Linear Programming Based Management Models for Optimal Land - Water - Crop System Planning , J. Water resources management , 20(6), 931 - 948 (2006) 6. Khare D., Jat M.K. and Sunder J.D., Assessment of water resourc es allocation options: Conjunctive use planning in a link canal command , J. Esources, Conser. and Recycling, Elsevier (2007) 7. Brian W. and Marshall G., The use of a coupled groundwater simulation and optimization model to guide groundwater management in the upper Klamath basin, Oregon and California. 2 nd Joint Federal Interagency Conference, Las Vegas, NV (2010) 8. Saafan T.A., Moharram S.H., Gad M.I. and Khalaf A.S.,A multi - objective optimization approach to groundwater management using genetic algorithm , Int . J. Water Res. and Env. Engg., 3 (7) , 139 - 149 ( 2011) 9. Ajay Singh , An overview of the optimization modelling applications , J. Hydro., 466(12), 167 - 182 (2012) 10. Regulwar D.G. and P radhan V.S., Irrigation Planning with Conjunctive Use of Surface and Groundwater Using Fuzzy Resources , J. Water Res. and Protection , 5, 816 - 822 (2013) 11. Li M. and Guo P. A., Multi - Objective Optimal Allocation Model for Irrigation Water Resources Under Multi ple Uncertainties. Applied Mathematical Modelling, Elsevier (2014) 12. Raul S.K. , Panda Sudhindra N. M. and Inamdar P. M., Sectoral Conjunctive Use Planning for Optimal Cropping under Hydrological Uncertainty , J. Irri. an d Dra. Engg. , 138(2), 145 - 155 (2014)